What is it about?
This paper presents a system for autonomous navigation of a quadcopter in indoor environments. A simultaneous localization and mapping (SLAM) algorithm is used to map the indoor environment and to determine the location of the quadcopter within the environment. A wall following system is used for the movement of the vehicle and collision avoidance. The SLAM system works in unison with the wall following system to navigate and at the same time map an unknown indoor environment. The performance of the developed system was tested in a simulated environment and methods for localization and mapping, wall following control, collision avoidance, data processing, and model verification have been presented.
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Why is it important?
UAVs are helpful accessing environments that humans or other larger vehicles cannot access or are too dangerous to enter such as indoor environments for search and rescue missions during natural disasters (flood, earthquake, or fire). UAVs are significantly cheaper than manned aircraft and reduce or eliminate risks to humans for many dangerous missions. To navigate such unknown environment without a preexisting map and without any knowledge of the environment is a complex problem. This paper presents the application of a wall following system for autonomous navigation of a quadcopter in the indoor environment and at the same time map the unknown environment. This will allow the UAV to autonomously navigate the indoor environment in the absence of GPS signal.
Perspectives
Autonomous navigation in an unknown environment is a difficult task to undertake. Even though there are numerous ways to execute the task of exploring an unknown environment, the main idea of this paper was to be able to follow the contours of a building form the inside and explore all possible areas within that building where the UAV could enter. The paper was able to develop a system, which executed a wall following algorithm to navigate within a building without collision, and at the same time, develop the map of the surrounding. The paper was also able to make recommendations on when and how turns are to be executed. Using only laser scanner data, the system was able to execute smoother turns by limiting the viewing angle and manipulating turns accordingly. The approach used in this paper was able to make turns smoother and sharper, which allowed the indoor environment and vehicle trajectory to be better traced and aligned as the UAV moved within it. Distance maintained to the wall, angle to the wall, turn distances and angles and yaw angle corrections of the UAV were studied in a simulated environment, analyzed, and presented in this paper.
Aditya Acharya
California State Polytechnic University, Pomona
Read the Original
This page is a summary of: Autonomous Navigation of a Quadcopter in Indoor Environments, January 2019, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/6.2019-1057.
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